Quality of Life Research

, Volume 22, Issue 8, pp 1973–1986 | Cite as

Psychometric and factor analytic evaluation of the 15D health-related quality of life instrument: the case of Greece

  • Fotios Anagnostopoulos
  • John Yfantopoulos
  • Irini Moustaki
  • Dimitris Niakas



To investigate the dimensionality, construct validity in the form of factorial, convergent, discriminant, and known-groups validity, as well as scale reliability of the fifteen dimensional (15D) instrument.


15D data were collected from a large Greek general population sample (N = 3,268) which was randomly split into two halves. Data from the first sample were used to examine the distributional properties of the 15 items, as well as the factor structure adopting an exploratory approach. Data from the second sample were used to perform a confirmatory factor analysis of the 15 items, examine the goodness of fit of several measurement models, and evaluate reliability and known-groups validity of the resulting subscales, along with convergent and discriminant validity of the constructs.


Exploratory factor analysis, using a distribution-free method, revealed a three-factor solution of the 15D (functional ability, physiological needs satisfaction, emotional well-being). Confirmatory factor analysis provided support for the three-factor solution but suggested that certain modifications should be made to this solution, involving freeing certain elements of the matrix of factor loadings and of the covariance matrix of measurement errors in the observed variables. Evidence of convergent validity was provided for all three factors, but discriminant validity was supported only for the emotional well-being construct. Scale reliability and known-groups validity of the resulting three subscales were satisfactory.


Our results confirm the multidimensional structure of the 15D and the existence of three latent factors that cover important aspects of the health-related quality of life domain (physical and emotional functioning). The implications of our results for the validity of the 15D and suggestions for future research are outlined.


15D Health-related quality of life Validity Factor analysis 



Health-related quality of life


Exploratory factor analysis


Confirmatory factor analysis


Parallel analysis


General self-rated health


Minimum residual method


Weighted least squares method


Root-mean-square error of approximation


Comparative fit index


Non-normed fit index


Expected cross-validation index


Akaike information criterion


Consistent Akaike information criterion


Average variance extracted


  1. 1.
    Centers for Disease Control and Prevention. (2000). Measuring healthy days: Population assessment of health-related quality of life. Atlanta: CDC.Google Scholar
  2. 2.
    Deshpande, A. D., Sefko, J. A., Jeffe, D. B., & Schootman, M. (2011). The association between chronic disease burden and quality of life among breast cancer survivors in Missouri. Breast Cancer Research and Treatment, 129(3), 877–886.PubMedCrossRefGoogle Scholar
  3. 3.
    Ziaian, T., Sawyer, M. G., Reynolds, K. E., et al. (2006). Treatment burden and health-related quality of life of children with diabetes, cystic fibrosis and asthma. Journal of Paediatrics and Child Health, 42(10), 596–600.PubMedCrossRefGoogle Scholar
  4. 4.
    Beckie, T. M., & Beckstead, J. W. (2011). The effects of a cardiac rehabilitation program tailored for women on their perceptions of health: A randomized clinical trial. Journal of Cardiopulmonary Rehabilitation and Prevention, 31, 25–34.PubMedCrossRefGoogle Scholar
  5. 5.
    Rogers, L. Q., Courneya, K. S., Paragi- Gururaja, R., Markwell, S. J., & Imeokparia, R. (2008). Lifestyle behaviors and perceived health among men with and without a diagnosis of prostate cancer: A population-based, cross-sectional study. BMC Public Health, 8, 23.PubMedCrossRefGoogle Scholar
  6. 6.
    Bowden, A., & Fox-Rushby, J. A. (2003). A systematic and critical review of the process of translation and adaptation of generic health-related quality of life measures in Africa, Asia, Eastern Europe, the Middle East, and South America. Social Science and Medicine, 57(7), 1289–1306.PubMedCrossRefGoogle Scholar
  7. 7.
    Anagnostopoulos, F., Niakas, D., & Tountas, Y. (2009). Comparison between exploratory factor-analytic and SEM-based approaches to constructing SF-36 summary scores. Quality of Life Research, 18, 53–63.PubMedCrossRefGoogle Scholar
  8. 8.
    Anagnostopoulos, F., Niakas, D., & Pappa, E. (2005). Construct validation of the Greek SF-36 health survey. Quality of Life Research, 14, 1959–1965.PubMedCrossRefGoogle Scholar
  9. 9.
    Yfantopoulos, J. (2001). The Greek version of the EuroQol (EQ-5D) instrument. Archives of Hellenic Medicine, 18, 180–191.Google Scholar
  10. 10.
    Yfantopoulos, J., & Sintonen, H. (2002). Comparison of the properties of the EQ-5D with the 15D in Finland and Greece. In A. L. Norinder, K. M. Pedersen, & P. Roos (Eds.), Proceedings of the 18th plenary meeting of the EuroQol group, Copenhagen 2001. Lund: The Swedish Institute of Health Economics.Google Scholar
  11. 11.
    Yfantopoulos, J. (2001). Validation and measurement of quality of life in Greece using EQ-15D. Archives of Hellenic Medicine, 18, 279–287.Google Scholar
  12. 12.
    Aletras, V. H., Kontodimopoulos, N., Niakas, D. A., Vagia, M. G., Pelteki, H. J., Karatzoglou, G. I., et al. (2009). Valuation and preliminary validation of the Greek 15D in a sample of patients with coronary artery disease. Value in Health, 12(4), 574–579.PubMedCrossRefGoogle Scholar
  13. 13.
    Chatterji, S., Ustün, B. L., Sadana, R., Salomon, J. A., Mathers, C. D., & Murray, C. J. L. (2002). The conceptual basis for measuring and reporting on health. Geneva: WHO.Google Scholar
  14. 14.
    Sintonen, H. (1994). The 15-D measure of health related quality of life: Reliability, validity and sensitivity of its health state descriptive system [working paper # 41]. West Heidelberg: Centre for Health Program Evaluation.Google Scholar
  15. 15.
    Sintonen, H. (1995). The 15-D measure of health related quality of life: II Feasibility, reliability and validity of its valuation system [working paper # 42]. West Heidelberg: Centre for Health Program Evaluation.Google Scholar
  16. 16.
    Haapamäki, J., Roine, R. P., Sintonen, H., Turunen, U., Färkkilä, M. A., & Arkkila, P. E. T. (2010). Health-related quality of life in inflammatory bowel disease measured with the generic 15D instrument. Quality of Life Research, 19(6), 919–928.PubMedCrossRefGoogle Scholar
  17. 17.
    Miettola, J., Niskanen, L. K., Viinamäki, H., Sintonen, H., & Kumpusalo, E. (2008). Metabolic syndrome is associated with impaired health-related quality of life: Lapinlahti 2005 study. Quality of Life Research, 17(8), 1055–1062.PubMedCrossRefGoogle Scholar
  18. 18.
    Kauppinen, R., Rissanen, P., & Sintonen, H. (2000). Agreement between a generic and disease-specific quality-of-life instrument: The 15D and the SGRQ in asthmatic patients. Quality of Life Research, 9(9), 997–1003.CrossRefGoogle Scholar
  19. 19.
    Haapaniemi, T., Sotaniemi, A., Sintonen, H., & Taimela, E. (2004). The generic 15D instrument is valid and feasible for measuring health related quality of life in Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 75(7), 976–983.PubMedCrossRefGoogle Scholar
  20. 20.
    Moock, J., & Kohlmann, T. (2008). Comparing preference-based quality-of-life measures: Results from rehabilitation patients with musculoskeletal, cardiovascular, or psychosomatic disorders. Quality of Life Research, 17, 485–495.PubMedCrossRefGoogle Scholar
  21. 21.
    Stavem, K., Frøland, S. S., & Hellum, K. B. (2005). Comparison of preference-based utilities of the 15D, EQ-5D and SF-6D in patients with HIV/AIDS. Quality of Life Research, 14(4), 971–980.PubMedCrossRefGoogle Scholar
  22. 22.
    Wittrup-Jensen, K. U., & Lauridsen, J. (2008). An assessment of two generic health-related quality of life (HRQoL) instruments in patients suffering from low back pain. Odense: University of Southern Denmark.Google Scholar
  23. 23.
    Hawthorne, G., Richardson, J., & Day, N. A. (2001). A comparison of the assessment of quality of life (AQoL) with four other generic utility instruments. Annals of Medicine, 33, 358–370.PubMedCrossRefGoogle Scholar
  24. 24.
    Sintonen, H. (2001). The 15D instrument of health-related quality of life: Properties and applications. Annals of Medicine, 33, 328–336.PubMedCrossRefGoogle Scholar
  25. 25.
    Jöreskog, K., Sörbom, D., du Toit, S., & du Toit, M. (2000). LISREL 8: New statistical features. Lincolnwood, IL: Scientific Software International Inc.Google Scholar
  26. 26.
    Hawthorne, G., Richardson, J., Day, N., & McNeil, H. (2000). Life and death: Theoretical and practical issues in using utility instruments [working paper 102]. Victoria: The Centre for Health Program Evaluation.Google Scholar
  27. 27.
    Hawthorne, G. (2006). Measuring incontinence in Australia. Canberra: Commonwealth of Australia.Google Scholar
  28. 28.
    Reise, S. P., Waller, N. G., & Comrey, A. L. (2000). Factor analysis and scale revision. Psychological Assessment, 12(3), 287–297.PubMedCrossRefGoogle Scholar
  29. 29.
    Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.CrossRefGoogle Scholar
  30. 30.
    Glorfeld, L. W. (1995). An improvement on Horn’s parallel analysis methodology for selecting the correct number of factors to retain. Educational and Psychological Measurement, 55, 377–393.CrossRefGoogle Scholar
  31. 31.
    Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, 191–205.CrossRefGoogle Scholar
  32. 32.
    Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: The Guilford Press.Google Scholar
  33. 33.
    Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461–483.CrossRefGoogle Scholar
  34. 34.
    Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23.PubMedCrossRefGoogle Scholar
  35. 35.
    Zahran, H. S., Kobau, R., Moriarty, D. G., et al. (2005). Health-related quality of life surveillance-United States, 1993–2002. Morbidity and Mortality Weekly Report, 54, ss-4.Google Scholar
  36. 36.
    DeSalvo, K. B., Bloser, N., Reynolds, K., He, J., & Muntner, P. (2006). Mortality prediction with a single general self-rated health question: A meta-analysis. Journal of General Internal Medicine, 21(3), 267–275.PubMedCrossRefGoogle Scholar
  37. 37.
    Sintonen, H. (2001). Comparing properties of the 15D and the EQ-5D in measuring health-related quality of life. Archives of Hellenic Medicine, 18, 156–160.Google Scholar
  38. 38.
    Jöreskog, K. G. (2003). Factor analysis by MINRES. Chicago, IL: Scientific Software International. Assessed June 1, 2011.
  39. 39.
    Jöreskog, K., & Sörbom, D. (1996). PRELIS 2: User’s reference guide. Chicago, IL: Scientific Software International Inc.Google Scholar
  40. 40.
    Jöreskog, K. G., & Sörbom, D. (2008). LISREL 8.80 for windows [Computer Software]. Lincolnwood, IL: Scientific Software International Inc.Google Scholar
  41. 41.
    Muthen, B. (1993). Goodness of fit with categorical and other nonnormal variables. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage.Google Scholar
  42. 42.
    Bartholomew, D., Knott, M., & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach (3rd ed.). Chichester: Wiley.CrossRefGoogle Scholar
  43. 43.
    Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  44. 44.
    Raykov, T. (2004). Behavioral scale reliability and measurement invariance evaluation using latent variable modeling. Behavior Therapy, 35, 299–331.CrossRefGoogle Scholar
  45. 45.
    Raykov, T. (2002). Analytic estimation of standard error and confidence interval for scale reliability. Multivariate Behavioral Research, 37, 89–103.CrossRefGoogle Scholar
  46. 46.
    McHorney, C. A., & Tarlov, A. R. (1995). Individual-patient monitoring in clinical practice: Are available health status surveys adequate? Quality of Life Research, 4, 293–307.PubMedCrossRefGoogle Scholar
  47. 47.
    D’Agostino, R., & Pearson, E. (1973). Tests for departures from normality: Empirical results for the distribution of √b1 and b2. Biometrika, 60, 613–622.Google Scholar
  48. 48.
    Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.Google Scholar
  49. 49.
    Nunnally, J., & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill.Google Scholar
  50. 50.
    Deacon, B. J., Abramowitz, J. S., Woods, C. M., & Tolin, D. F. (2003). The anxiety sensitivity index-revised: Psychometric properties and factor structure in two nonclinical samples. Behaviour Research and Therapy, 41(12), 1427–1449.PubMedCrossRefGoogle Scholar
  51. 51.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  52. 52.
    Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.CrossRefGoogle Scholar
  53. 53.
    Saarni, S. I., Härkänen, T., Sintonen, H., Suvisaari, J., Koskinen, S., Aromaa, A., et al. (2006). The impact of 29 chronic conditions on health-related quality of life: A general population survey in Finland using 15D and EQ-5D. Quality of Life Research, 15(8), 1403–1414.PubMedCrossRefGoogle Scholar
  54. 54.
    Akinci, F., Yildirim, A., Ogutman, B., Ates, M., Gozu, H., Deyneli, O., et al. (2005). Translation, cultural adaptation, initial reliability and validation of Turkish 15D’s version: A generic health-related quality of life (HRQoL) instrument. Evaluation and the Health Professions, 28, 53–66.PubMedCrossRefGoogle Scholar
  55. 55.
    Hawthorne, G., Richardson, J., Osborne, R., & McNeil, H. (1997). The assessment of quality of life (AQoL) instrument: Construction, initial validation, and utility scaling [working paper 76]. Victoria: The Centre for Health Program Evaluation.Google Scholar
  56. 56.
    Hall, T., Krahn, G. L., Horner-Johnson, W., & Lamb, G. (2011). Examining functional content in widely used health-related quality of life scales. Rehabilitation Psychology, 56(2), 94–99.PubMedCrossRefGoogle Scholar
  57. 57.
    Richardson, J. (2010). Psychometric validity and multi-attribute utility (MAU) instruments [research paper 57]. Victoria: The Centre for Health Economics, Monash University.Google Scholar
  58. 58.
    Richardson, J., McKie, J., & Bariola, E. (2011). Review and critique of health-related multi-attribute utility instruments [research paper 64]. Victoria: The Centre for Health Economics, Monash University.Google Scholar
  59. 59.
    Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods, 14(2), 370–388.CrossRefGoogle Scholar
  60. 60.
    Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12(2), 205–218.PubMedCrossRefGoogle Scholar
  61. 61.
    Richardson, J., & Iezzi, A. (2011). Psychometric validity and the AQoL-8D multi-attribute utility instrument [research paper 71]. Victoria: The Centre for Health Economics, Monash University.Google Scholar
  62. 62.
    Peacock, S., Richardson, J., Day, N. A., Hawthorne, G., Iezzi, A., & Elsworth, G. (2010). Construction of the descriptive system for the Assessment of Quality of Life AQoL-6D utility instrument [research paper 49]. Victoria: The Centre for Health Economics, Monash University.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Fotios Anagnostopoulos
    • 1
  • John Yfantopoulos
    • 2
  • Irini Moustaki
    • 3
  • Dimitris Niakas
    • 4
  1. 1.Department of PsychologyPanteion University of Social and Political SciencesAthensGreece
  2. 2.School of Law, Economics, and Political ScienceUniversity of AthensAthensGreece
  3. 3.Department of StatisticsLondon School of EconomicsLondonUK
  4. 4.Faculty of Social SciencesHellenic Open UniversityPatrasGreece

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